Taxonomy Learning Using Word Sense Induction

Taxonomies are an important resource for a variety of Natural Language Processing (NLP) applications. Despite this, the current stateof-the-art methods in taxonomy learning have disregarded word polysemy, in effect, developing taxonomies that conflate word senses. In this paper, we present an unsupervised method that builds a taxonomy of senses learned automatically from an unlabelled corpus. Our evaluation on two WordNet-derived taxonomies shows that the learned taxonomies capture a higher number of correct taxonomic relations compared to those produced by traditional distributional similarity approaches that merge senses by grouping the features of each word into a single vector.

• Type: Journal
• Year: 2010
• Where: NAACL
• Authors: Ioannis P. Klapaftis, Suresh Manandhar
• Keywords: Computational Linguistics, Correct Taxonomic Relations, Current Stateof-the-art Methods, NAACL 2010, Taxonomies